Overview

Dataset statistics

Number of variables54
Number of observations132977
Missing cells525925
Missing cells (%)7.3%
Duplicate rows101
Duplicate rows (%)0.1%
Total size in memory55.8 MiB
Average record size in memory440.0 B

Variable types

Numeric28
Categorical26

Warnings

an has constant value "2019" Constant
Dataset has 101 (0.1%) duplicate rows Duplicates
id_vehicule has a high cardinality: 98670 distinct values High cardinality
hrmn has a high cardinality: 1386 distinct values High cardinality
dep has a high cardinality: 107 distinct values High cardinality
com has a high cardinality: 11444 distinct values High cardinality
adr has a high cardinality: 32020 distinct values High cardinality
lat has a high cardinality: 55671 distinct values High cardinality
long has a high cardinality: 56211 distinct values High cardinality
voie has a high cardinality: 14492 distinct values High cardinality
pr has a high cardinality: 489 distinct values High cardinality
pr1 has a high cardinality: 1103 distinct values High cardinality
occutc is highly correlated with larroutHigh correlation
larrout is highly correlated with occutcHigh correlation
lum is highly correlated with anHigh correlation
an is highly correlated with lum and 14 other fieldsHigh correlation
senc is highly correlated with anHigh correlation
grav is highly correlated with anHigh correlation
plan is highly correlated with anHigh correlation
etatp is highly correlated with anHigh correlation
catu is highly correlated with anHigh correlation
v1 is highly correlated with anHigh correlation
v2 is highly correlated with anHigh correlation
vosp is highly correlated with anHigh correlation
sexe is highly correlated with anHigh correlation
num_veh is highly correlated with anHigh correlation
agg is highly correlated with anHigh correlation
prof is highly correlated with anHigh correlation
circ is highly correlated with anHigh correlation
actp is highly correlated with anHigh correlation
occutc has 131099 (98.6%) missing values Missing
voie has 5914 (4.4%) missing values Missing
v2 has 123235 (92.7%) missing values Missing
lartpc has 132510 (99.6%) missing values Missing
larrout has 132146 (99.4%) missing values Missing
id_vehicule is uniformly distributed Uniform
lat is uniformly distributed Uniform
long is uniformly distributed Uniform
trajet has 34376 (25.9%) zeros Zeros
secu1 has 7196 (5.4%) zeros Zeros
secu2 has 58933 (44.3%) zeros Zeros
locp has 71472 (53.7%) zeros Zeros
obs has 111797 (84.1%) zeros Zeros
obsm has 25195 (18.9%) zeros Zeros
choc has 6844 (5.1%) zeros Zeros
manv has 7140 (5.4%) zeros Zeros
motor has 13538 (10.2%) zeros Zeros
nbv has 3476 (2.6%) zeros Zeros
infra has 110523 (83.1%) zeros Zeros

Reproduction

Analysis started2021-03-18 14:54:28.875009
Analysis finished2021-03-18 14:56:26.882641
Duration1 minute and 58.01 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Num_Acc
Real number (ℝ≥0)

Distinct58840
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.019000294 × 1011
Minimum2.019 × 1011
Maximum2.019000588 × 1011
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:26.978557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.019 × 1011
5-th percentile2.01900003 × 1011
Q12.019000147 × 1011
median2.019000294 × 1011
Q32.019000442 × 1011
95-th percentile2.019000559 × 1011
Maximum2.019000588 × 1011
Range58839
Interquartile range (IQR)29478

Descriptive statistics

Standard deviation16994.43687
Coefficient of variation (CV)8.417253292 × 108
Kurtosis-1.202351012
Mean2.019000294 × 1011
Median Absolute Deviation (MAD)14742
Skewness0.001403624885
Sum2.684806021 × 1016
Variance288810884.7
MonotocityIncreasing
2021-03-18T15:56:27.092725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.019000497 × 101133
 
< 0.1%
2.019000296 × 101130
 
< 0.1%
2.019000271 × 101125
 
< 0.1%
2.019000522 × 101124
 
< 0.1%
2.019000546 × 101124
 
< 0.1%
2.019000499 × 101124
 
< 0.1%
2.019000576 × 101124
 
< 0.1%
2.019000414 × 101123
 
< 0.1%
2.01900035 × 101123
 
< 0.1%
2.019000429 × 101123
 
< 0.1%
Other values (58830)132724
99.8%
ValueCountFrequency (%)
2.019 × 10113
< 0.1%
2.019 × 10111
 
< 0.1%
2.019 × 10114
< 0.1%
2.019 × 10114
< 0.1%
2.019 × 10113
< 0.1%
ValueCountFrequency (%)
2.019000588 × 10112
< 0.1%
2.019000588 × 10111
 
< 0.1%
2.019000588 × 10111
 
< 0.1%
2.019000588 × 10113
< 0.1%
2.019000588 × 10113
< 0.1%

id_vehicule
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98670
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
138 212 691
 
33
138 250 566
 
30
138 255 393
 
25
138 212 361
 
23
138 225 436
 
22
Other values (98665)
132844 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1462747
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73492 ?
Unique (%)55.3%

Sample

1st row138 306 524
2nd row138 306 524
3rd row138 306 525
4th row138 306 523
5th row138 306 520
ValueCountFrequency (%)
138 212 69133
 
< 0.1%
138 250 56630
 
< 0.1%
138 255 39325
 
< 0.1%
138 212 36123
 
< 0.1%
138 225 43622
 
< 0.1%
138 240 29322
 
< 0.1%
138 232 65320
 
< 0.1%
138 254 81920
 
< 0.1%
138 273 54219
 
< 0.1%
138 266 05419
 
< 0.1%
Other values (98660)132744
99.8%
2021-03-18T15:56:27.508777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
138133076
33.4%
3001474
 
0.4%
2121464
 
0.4%
2471441
 
0.4%
2001439
 
0.4%
2881434
 
0.4%
2911427
 
0.4%
2931420
 
0.4%
2961413
 
0.4%
1971411
 
0.4%
Other values (990)252932
63.4%

Most occurring characters

ValueCountFrequency (%)
 265954
18.2%
3206157
14.1%
1203753
13.9%
8198183
13.5%
2184806
12.6%
073274
 
5.0%
969993
 
4.8%
665608
 
4.5%
565484
 
4.5%
764947
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1196793
81.8%
Space Separator265954
 
18.2%

Most frequent character per category

ValueCountFrequency (%)
3206157
17.2%
1203753
17.0%
8198183
16.6%
2184806
15.4%
073274
 
6.1%
969993
 
5.8%
665608
 
5.5%
565484
 
5.5%
764947
 
5.4%
464588
 
5.4%
ValueCountFrequency (%)
 265954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1462747
100.0%

Most frequent character per script

ValueCountFrequency (%)
 265954
18.2%
3206157
14.1%
1203753
13.9%
8198183
13.5%
2184806
12.6%
073274
 
5.0%
969993
 
4.8%
665608
 
4.5%
565484
 
4.5%
764947
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1196793
81.8%
None265954
 
18.2%

Most frequent character per block

ValueCountFrequency (%)
3206157
17.2%
1203753
17.0%
8198183
16.6%
2184806
15.4%
073274
 
6.1%
969993
 
5.8%
665608
 
5.5%
565484
 
5.5%
764947
 
5.4%
464588
 
5.4%
ValueCountFrequency (%)
 265954
100.0%

num_veh
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
A01
80464 
B01
44394 
C01
 
5465
D01
 
1205
Z01
 
814
Other values (24)
 
635

Length

Max length4
Median length3
Mean length3.0000376
Min length3

Characters and Unicode

Total characters398936
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowB01
2nd rowB01
3rd rowA01
4th rowA01
5th rowA01
ValueCountFrequency (%)
A0180464
60.5%
B0144394
33.4%
C015465
 
4.1%
D011205
 
0.9%
Z01814
 
0.6%
E01292
 
0.2%
F01113
 
0.1%
Y0163
 
< 0.1%
G0147
 
< 0.1%
H0122
 
< 0.1%
Other values (19)98
 
0.1%
2021-03-18T15:56:27.695217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a0180464
60.5%
b0144394
33.4%
c015465
 
4.1%
d011205
 
0.9%
z01814
 
0.6%
e01292
 
0.2%
f01113
 
0.1%
y0163
 
< 0.1%
g0147
 
< 0.1%
h0122
 
< 0.1%
Other values (19)98
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0132977
33.3%
1132977
33.3%
A80466
20.2%
B44396
 
11.1%
C5466
 
1.4%
D1205
 
0.3%
Z814
 
0.2%
E292
 
0.1%
F114
 
< 0.1%
Y63
 
< 0.1%
Other values (17)166
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number265954
66.7%
Uppercase Letter132981
33.3%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A80466
60.5%
B44396
33.4%
C5466
 
4.1%
D1205
 
0.9%
Z814
 
0.6%
E292
 
0.2%
F114
 
0.1%
Y63
 
< 0.1%
G47
 
< 0.1%
H22
 
< 0.1%
Other values (14)96
 
0.1%
ValueCountFrequency (%)
0132977
50.0%
1132977
50.0%
ValueCountFrequency (%)
\1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common265955
66.7%
Latin132981
33.3%

Most frequent character per script

ValueCountFrequency (%)
A80466
60.5%
B44396
33.4%
C5466
 
4.1%
D1205
 
0.9%
Z814
 
0.6%
E292
 
0.2%
F114
 
0.1%
Y63
 
< 0.1%
G47
 
< 0.1%
H22
 
< 0.1%
Other values (14)96
 
0.1%
ValueCountFrequency (%)
0132977
50.0%
1132977
50.0%
\1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII398936
100.0%

Most frequent character per block

ValueCountFrequency (%)
0132977
33.3%
1132977
33.3%
A80466
20.2%
B44396
 
11.1%
C5466
 
1.4%
D1205
 
0.3%
Z814
 
0.2%
E292
 
0.1%
F114
 
< 0.1%
Y63
 
< 0.1%
Other values (17)166
 
< 0.1%

place
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.184753754
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:27.770087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.695723955
Coefficient of variation (CV)1.233879996
Kurtosis3.705101663
Mean2.184753754
Median Absolute Deviation (MAD)0
Skewness2.310252614
Sum290522
Variance7.266927639
MonotocityNot monotonic
2021-03-18T15:56:27.837737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
196935
72.9%
215495
 
11.7%
1011265
 
8.5%
32530
 
1.9%
42301
 
1.7%
91539
 
1.2%
71355
 
1.0%
5763
 
0.6%
8619
 
0.5%
6175
 
0.1%
ValueCountFrequency (%)
196935
72.9%
215495
 
11.7%
32530
 
1.9%
42301
 
1.7%
5763
 
0.6%
ValueCountFrequency (%)
1011265
8.5%
91539
 
1.2%
8619
 
0.5%
71355
 
1.0%
6175
 
0.1%

catu
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
97356 
2
24356 
3
11265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132977
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%
2021-03-18T15:56:27.996351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:28.048284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%

Most occurring characters

ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
100.0%

Most frequent character per category

ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common132977
100.0%

Most frequent character per script

ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII132977
100.0%

Most frequent character per block

ValueCountFrequency (%)
197356
73.2%
224356
 
18.3%
311265
 
8.5%

grav
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
55314 
4
53307 
3
20858 
2
 
3498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132977
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row1
4th row4
5th row1
ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%
2021-03-18T15:56:28.190313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:28.242625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%

Most occurring characters

ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
100.0%

Most frequent character per category

ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common132977
100.0%

Most frequent character per script

ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII132977
100.0%

Most frequent character per block

ValueCountFrequency (%)
155314
41.6%
453307
40.1%
320858
 
15.7%
23498
 
2.6%

sexe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
90384 
2
42593 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132977
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1
ValueCountFrequency (%)
190384
68.0%
242593
32.0%
2021-03-18T15:56:28.380887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:28.431446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
190384
68.0%
242593
32.0%

Most occurring characters

ValueCountFrequency (%)
190384
68.0%
242593
32.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
100.0%

Most frequent character per category

ValueCountFrequency (%)
190384
68.0%
242593
32.0%

Most occurring scripts

ValueCountFrequency (%)
Common132977
100.0%

Most frequent character per script

ValueCountFrequency (%)
190384
68.0%
242593
32.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII132977
100.0%

Most frequent character per block

ValueCountFrequency (%)
190384
68.0%
242593
32.0%

an_nais
Real number (ℝ≥0)

Distinct104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.0796
Minimum1900
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:28.495946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1944
Q11967
median1983
Q31995
95-th percentile2005
Maximum2019
Range119
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.1646246
Coefficient of variation (CV)0.009678714231
Kurtosis-0.06969051124
Mean1980.0796
Median Absolute Deviation (MAD)14
Skewness-0.5903380339
Sum263305045
Variance367.2828362
MonotocityNot monotonic
2021-03-18T15:56:28.602662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20003828
 
2.9%
19993699
 
2.8%
19983619
 
2.7%
19973534
 
2.7%
19963468
 
2.6%
19953282
 
2.5%
19943102
 
2.3%
19933058
 
2.3%
19922990
 
2.2%
19902933
 
2.2%
Other values (94)99464
74.8%
ValueCountFrequency (%)
1900133
0.1%
190143
 
< 0.1%
19111
 
< 0.1%
19193
 
< 0.1%
19205
 
< 0.1%
ValueCountFrequency (%)
2019210
0.2%
2018330
0.2%
2017374
0.3%
2016372
0.3%
2015431
0.3%

trajet
Real number (ℝ)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.224279387
Minimum-1
Maximum9
Zeros34376
Zeros (%)25.9%
Memory size2.0 MiB
2021-03-18T15:56:28.689828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median4
Q35
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.667380018
Coefficient of variation (CV)0.8272794316
Kurtosis-0.7155854079
Mean3.224279387
Median Absolute Deviation (MAD)1
Skewness0.288085008
Sum428755
Variance7.114916162
MonotocityNot monotonic
2021-03-18T15:56:28.760071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
552021
39.1%
034376
25.9%
117677
 
13.3%
412506
 
9.4%
99461
 
7.1%
33557
 
2.7%
22836
 
2.1%
-1543
 
0.4%
ValueCountFrequency (%)
-1543
 
0.4%
034376
25.9%
117677
13.3%
22836
 
2.1%
33557
 
2.7%
ValueCountFrequency (%)
99461
 
7.1%
552021
39.1%
412506
 
9.4%
33557
 
2.7%
22836
 
2.1%

secu1
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.17979049
Minimum-1
Maximum9
Zeros7196
Zeros (%)5.4%
Memory size2.0 MiB
2021-03-18T15:56:28.830785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median1
Q32
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.47462694
Coefficient of variation (CV)1.135259077
Kurtosis1.597259904
Mean2.17979049
Median Absolute Deviation (MAD)0
Skewness1.819540554
Sum289862
Variance6.123778494
MonotocityNot monotonic
2021-03-18T15:56:28.904924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
180550
60.6%
224377
 
18.3%
819288
 
14.5%
07196
 
5.4%
3931
 
0.7%
9198
 
0.1%
5144
 
0.1%
6109
 
0.1%
493
 
0.1%
-188
 
0.1%
ValueCountFrequency (%)
-188
 
0.1%
07196
 
5.4%
180550
60.6%
224377
 
18.3%
3931
 
0.7%
ValueCountFrequency (%)
9198
 
0.1%
819288
14.5%
73
 
< 0.1%
6109
 
0.1%
5144
 
0.1%

secu2
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.454281567
Minimum-1
Maximum9
Zeros58933
Zeros (%)44.3%
Memory size2.0 MiB
2021-03-18T15:56:28.978990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q33
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.284485303
Coefficient of variation (CV)2.258493387
Kurtosis-0.332462059
Mean1.454281567
Median Absolute Deviation (MAD)1
Skewness1.196954897
Sum193386
Variance10.78784371
MonotocityNot monotonic
2021-03-18T15:56:29.050811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
058933
44.3%
-140164
30.2%
817840
 
13.4%
611872
 
8.9%
51843
 
1.4%
41072
 
0.8%
9369
 
0.3%
2346
 
0.3%
7218
 
0.2%
1202
 
0.2%
ValueCountFrequency (%)
-140164
30.2%
058933
44.3%
1202
 
0.2%
2346
 
0.3%
3118
 
0.1%
ValueCountFrequency (%)
9369
 
0.3%
817840
13.4%
7218
 
0.2%
611872
8.9%
51843
 
1.4%

secu3
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.9133609572
Minimum-1
Maximum9
Zeros612
Zeros (%)0.5%
Memory size2.0 MiB
2021-03-18T15:56:29.122302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8920616072
Coefficient of variation (CV)-0.9766802491
Kurtosis114.9772392
Mean-0.9133609572
Median Absolute Deviation (MAD)0
Skewness10.76229717
Sum-121456
Variance0.795773911
MonotocityNot monotonic
2021-03-18T15:56:29.193426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-1131223
98.7%
9960
 
0.7%
0612
 
0.5%
880
 
0.1%
662
 
< 0.1%
416
 
< 0.1%
113
 
< 0.1%
54
 
< 0.1%
34
 
< 0.1%
23
 
< 0.1%
ValueCountFrequency (%)
-1131223
98.7%
0612
 
0.5%
113
 
< 0.1%
23
 
< 0.1%
34
 
< 0.1%
ValueCountFrequency (%)
9960
0.7%
880
 
0.1%
662
 
< 0.1%
54
 
< 0.1%
416
 
< 0.1%

locp
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1092670161
Minimum-1
Maximum9
Zeros71472
Zeros (%)53.7%
Memory size2.0 MiB
2021-03-18T15:56:29.265564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q30
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.247776118
Coefficient of variation (CV)-11.41951306
Kurtosis17.43970943
Mean-0.1092670161
Median Absolute Deviation (MAD)0
Skewness3.545760117
Sum-14530
Variance1.55694524
MonotocityNot monotonic
2021-03-18T15:56:30.026731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
071472
53.7%
-150473
38.0%
33423
 
2.6%
22491
 
1.9%
41641
 
1.2%
11604
 
1.2%
5793
 
0.6%
9575
 
0.4%
6320
 
0.2%
8169
 
0.1%
ValueCountFrequency (%)
-150473
38.0%
071472
53.7%
11604
 
1.2%
22491
 
1.9%
33423
 
2.6%
ValueCountFrequency (%)
9575
0.4%
8169
 
0.1%
716
 
< 0.1%
6320
0.2%
5793
0.6%

actp
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
95554 
-1
26341 
3
 
8162
1
 
701
9
 
688
Other values (8)
 
1531

Length

Max length3
Median length1
Mean length1.396173774
Min length1

Characters and Unicode

Total characters185659
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row -1
2nd row -1
3rd row -1
4th row -1
5th row0
ValueCountFrequency (%)
095554
71.9%
-126341
 
19.8%
38162
 
6.1%
1701
 
0.5%
9688
 
0.5%
5453
 
0.3%
B431
 
0.3%
2313
 
0.2%
4167
 
0.1%
A107
 
0.1%
Other values (3)60
 
< 0.1%
2021-03-18T15:56:30.196589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
095554
71.9%
127042
 
20.3%
38162
 
6.1%
9688
 
0.5%
5453
 
0.3%
b431
 
0.3%
2313
 
0.2%
4167
 
0.1%
a107
 
0.1%
629
 
< 0.1%
Other values (2)31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
095554
51.5%
127042
 
14.6%
26341
 
14.2%
-26341
 
14.2%
38162
 
4.4%
9688
 
0.4%
5453
 
0.2%
B431
 
0.2%
2313
 
0.2%
4167
 
0.1%
Other values (4)167
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132439
71.3%
Space Separator26341
 
14.2%
Dash Punctuation26341
 
14.2%
Uppercase Letter538
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
095554
72.1%
127042
 
20.4%
38162
 
6.2%
9688
 
0.5%
5453
 
0.3%
2313
 
0.2%
4167
 
0.1%
629
 
< 0.1%
718
 
< 0.1%
813
 
< 0.1%
ValueCountFrequency (%)
B431
80.1%
A107
 
19.9%
ValueCountFrequency (%)
26341
100.0%
ValueCountFrequency (%)
-26341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common185121
99.7%
Latin538
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
095554
51.6%
127042
 
14.6%
26341
 
14.2%
-26341
 
14.2%
38162
 
4.4%
9688
 
0.4%
5453
 
0.2%
2313
 
0.2%
4167
 
0.1%
629
 
< 0.1%
Other values (2)31
 
< 0.1%
ValueCountFrequency (%)
B431
80.1%
A107
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII185659
100.0%

Most frequent character per block

ValueCountFrequency (%)
095554
51.5%
127042
 
14.6%
26341
 
14.2%
-26341
 
14.2%
38162
 
4.4%
9688
 
0.4%
5453
 
0.2%
B431
 
0.2%
2313
 
0.2%
4167
 
0.1%
Other values (4)167
 
0.1%

etatp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
-1
121920 
1
 
8392
2
 
2219
3
 
446

Length

Max length2
Median length2
Mean length1.916850282
Min length1

Characters and Unicode

Total characters254897
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1121920
91.7%
18392
 
6.3%
22219
 
1.7%
3446
 
0.3%
2021-03-18T15:56:30.363714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:30.418536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1130312
98.0%
22219
 
1.7%
3446
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1130312
51.1%
-121920
47.8%
22219
 
0.9%
3446
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
52.2%
Dash Punctuation121920
47.8%

Most frequent character per category

ValueCountFrequency (%)
1130312
98.0%
22219
 
1.7%
3446
 
0.3%
ValueCountFrequency (%)
-121920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common254897
100.0%

Most frequent character per script

ValueCountFrequency (%)
1130312
51.1%
-121920
47.8%
22219
 
0.9%
3446
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII254897
100.0%

Most frequent character per block

ValueCountFrequency (%)
1130312
51.1%
-121920
47.8%
22219
 
0.9%
3446
 
0.2%

senc
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
58983 
2
44431 
3
19918 
0
9599 
-1
 
46

Length

Max length2
Median length1
Mean length1.000345924
Min length1

Characters and Unicode

Total characters133023
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1
ValueCountFrequency (%)
158983
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%
-146
 
< 0.1%
2021-03-18T15:56:30.573792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:30.629295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
159029
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%

Most occurring characters

ValueCountFrequency (%)
159029
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%
-46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
> 99.9%
Dash Punctuation46
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
159029
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%
ValueCountFrequency (%)
-46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133023
100.0%

Most frequent character per script

ValueCountFrequency (%)
159029
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%
-46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133023
100.0%

Most frequent character per block

ValueCountFrequency (%)
159029
44.4%
244431
33.4%
319918
 
15.0%
09599
 
7.2%
-46
 
< 0.1%

catv
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.20498282
Minimum0
Maximum99
Zeros247
Zeros (%)0.2%
Memory size2.0 MiB
2021-03-18T15:56:30.705557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median7
Q310
95-th percentile33
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.33929501
Coefficient of variation (CV)1.011004701
Kurtosis11.26149337
Mean12.20498282
Median Absolute Deviation (MAD)0
Skewness2.74069144
Sum1622982
Variance152.2582013
MonotocityNot monotonic
2021-03-18T15:56:30.800551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
785253
64.1%
339257
 
7.0%
108790
 
6.6%
15265
 
4.0%
24268
 
3.2%
303614
 
2.7%
322824
 
2.1%
312117
 
1.6%
341596
 
1.2%
371343
 
1.0%
Other values (21)8650
 
6.5%
ValueCountFrequency (%)
0247
 
0.2%
15265
 
4.0%
24268
 
3.2%
31192
 
0.9%
785253
64.1%
ValueCountFrequency (%)
99551
0.4%
80264
 
0.2%
60144
 
0.1%
50731
0.5%
43563
0.4%

obs
Real number (ℝ)

ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.120035796
Minimum-1
Maximum17
Zeros111797
Zeros (%)84.1%
Memory size2.0 MiB
2021-03-18T15:56:30.887239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum17
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.263112977
Coefficient of variation (CV)2.913400616
Kurtosis9.202810686
Mean1.120035796
Median Absolute Deviation (MAD)0
Skewness3.179723126
Sum148939
Variance10.6479063
MonotocityNot monotonic
2021-03-18T15:56:30.965636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0111797
84.1%
13068
 
2.3%
132770
 
2.1%
22287
 
1.7%
32158
 
1.6%
42016
 
1.5%
61717
 
1.3%
81653
 
1.2%
141031
 
0.8%
12996
 
0.7%
Other values (9)3484
 
2.6%
ValueCountFrequency (%)
-143
 
< 0.1%
0111797
84.1%
13068
 
2.3%
22287
 
1.7%
32158
 
1.6%
ValueCountFrequency (%)
17235
 
0.2%
16593
 
0.4%
15820
 
0.6%
141031
 
0.8%
132770
2.1%

obsm
Real number (ℝ)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.54324432
Minimum-1
Maximum9
Zeros25195
Zeros (%)18.9%
Memory size2.0 MiB
2021-03-18T15:56:31.044057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q32
95-th percentile2
Maximum9
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.089174499
Coefficient of variation (CV)0.7057693226
Kurtosis16.16559222
Mean1.54324432
Median Absolute Deviation (MAD)0
Skewness2.343837035
Sum205216
Variance1.186301088
MonotocityNot monotonic
2021-03-18T15:56:31.114977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
284950
63.9%
025195
 
18.9%
120758
 
15.6%
91003
 
0.8%
6809
 
0.6%
499
 
0.1%
-189
 
0.1%
574
 
0.1%
ValueCountFrequency (%)
-189
 
0.1%
025195
 
18.9%
120758
 
15.6%
284950
63.9%
499
 
0.1%
ValueCountFrequency (%)
91003
 
0.8%
6809
 
0.6%
574
 
0.1%
499
 
0.1%
284950
63.9%

choc
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.91280447
Minimum-1
Maximum9
Zeros6844
Zeros (%)5.1%
Memory size2.0 MiB
2021-03-18T15:56:31.187558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.391688698
Coefficient of variation (CV)0.8210948323
Kurtosis-0.08438043282
Mean2.91280447
Median Absolute Deviation (MAD)1
Skewness1.008702139
Sum387336
Variance5.720174827
MonotocityNot monotonic
2021-03-18T15:56:31.257548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
148016
36.1%
320156
15.2%
217269
 
13.0%
413702
 
10.3%
88751
 
6.6%
77523
 
5.7%
06844
 
5.1%
64659
 
3.5%
53785
 
2.8%
92223
 
1.7%
ValueCountFrequency (%)
-149
 
< 0.1%
06844
 
5.1%
148016
36.1%
217269
 
13.0%
320156
15.2%
ValueCountFrequency (%)
92223
 
1.7%
88751
6.6%
77523
5.7%
64659
3.5%
53785
2.8%

manv
Real number (ℝ)

ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.202343262
Minimum-1
Maximum26
Zeros7140
Zeros (%)5.4%
Memory size2.0 MiB
2021-03-18T15:56:31.333502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q315
95-th percentile23
Maximum26
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.11396764
Coefficient of variation (CV)1.126573303
Kurtosis-0.68626766
Mean7.202343262
Median Absolute Deviation (MAD)1
Skewness0.8694529984
Sum957746
Variance65.83647087
MonotocityNot monotonic
2021-03-18T15:56:31.421819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
155866
42.0%
214801
 
11.1%
1510468
 
7.9%
07140
 
5.4%
135871
 
4.4%
264900
 
3.7%
174496
 
3.4%
233644
 
2.7%
93612
 
2.7%
163477
 
2.6%
Other values (18)18702
 
14.1%
ValueCountFrequency (%)
-146
 
< 0.1%
07140
 
5.4%
155866
42.0%
214801
 
11.1%
31271
 
1.0%
ValueCountFrequency (%)
264900
3.7%
25331
 
0.2%
24519
 
0.4%
233644
2.7%
22400
 
0.3%

motor
Real number (ℝ)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.149943223
Minimum-1
Maximum6
Zeros13538
Zeros (%)10.2%
Memory size2.0 MiB
2021-03-18T15:56:31.498376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median1
Q31
95-th percentile5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.054699399
Coefficient of variation (CV)0.9171751944
Kurtosis10.51705587
Mean1.149943223
Median Absolute Deviation (MAD)0
Skewness3.151816464
Sum152916
Variance1.112390823
MonotocityNot monotonic
2021-03-18T15:56:31.568169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1109235
82.1%
013538
 
10.2%
54707
 
3.5%
62041
 
1.5%
31922
 
1.4%
21076
 
0.8%
-1370
 
0.3%
488
 
0.1%
ValueCountFrequency (%)
-1370
 
0.3%
013538
 
10.2%
1109235
82.1%
21076
 
0.8%
31922
 
1.4%
ValueCountFrequency (%)
62041
1.5%
54707
3.5%
488
 
0.1%
31922
1.4%
21076
 
0.8%

occutc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)1.2%
Missing131099
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean5.600106496
Minimum0
Maximum33
Zeros10
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-18T15:56:31.648371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile22
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.770642675
Coefficient of variation (CV)1.387588375
Kurtosis2.743413389
Mean5.600106496
Median Absolute Deviation (MAD)1
Skewness1.898899762
Sum10517
Variance60.38288758
MonotocityNot monotonic
2021-03-18T15:56:31.725341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1901
 
0.7%
2185
 
0.1%
3152
 
0.1%
490
 
0.1%
580
 
0.1%
1854
 
< 0.1%
2244
 
< 0.1%
1938
 
< 0.1%
937
 
< 0.1%
1734
 
< 0.1%
Other values (12)263
 
0.2%
(Missing)131099
98.6%
ValueCountFrequency (%)
010
 
< 0.1%
1901
0.7%
2185
 
0.1%
3152
 
0.1%
490
 
0.1%
ValueCountFrequency (%)
3333
< 0.1%
3030
< 0.1%
2323
< 0.1%
2244
< 0.1%
2020
< 0.1%

jour
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.67954609
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:31.812560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.746306394
Coefficient of variation (CV)0.5578163006
Kurtosis-1.176064264
Mean15.67954609
Median Absolute Deviation (MAD)8
Skewness0.02225435625
Sum2085019
Variance76.49787554
MonotocityNot monotonic
2021-03-18T15:56:31.897255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
134628
 
3.5%
184606
 
3.5%
124569
 
3.4%
74569
 
3.4%
154559
 
3.4%
44500
 
3.4%
204496
 
3.4%
54478
 
3.4%
64453
 
3.3%
164431
 
3.3%
Other values (21)87688
65.9%
ValueCountFrequency (%)
14185
3.1%
24394
3.3%
34008
3.0%
44500
3.4%
54478
3.4%
ValueCountFrequency (%)
312586
1.9%
304049
3.0%
294073
3.1%
284031
3.0%
274094
3.1%

mois
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.701587493
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:31.981187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.385576978
Coefficient of variation (CV)0.5051902973
Kurtosis-1.16018346
Mean6.701587493
Median Absolute Deviation (MAD)3
Skewness-0.06951767333
Sum891157
Variance11.46213147
MonotocityNot monotonic
2021-03-18T15:56:32.053757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
712535
9.4%
612433
9.3%
1012093
9.1%
911882
8.9%
1211576
8.7%
1111137
8.4%
510840
8.2%
310518
7.9%
810457
7.9%
410340
7.8%
Other values (2)19166
14.4%
ValueCountFrequency (%)
19575
7.2%
29591
7.2%
310518
7.9%
410340
7.8%
510840
8.2%
ValueCountFrequency (%)
1211576
8.7%
1111137
8.4%
1012093
9.1%
911882
8.9%
810457
7.9%

an
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2019
132977 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters531908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019
ValueCountFrequency (%)
2019132977
100.0%
2021-03-18T15:56:32.209538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:32.259064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2019132977
100.0%

Most occurring characters

ValueCountFrequency (%)
2132977
25.0%
0132977
25.0%
1132977
25.0%
9132977
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number531908
100.0%

Most frequent character per category

ValueCountFrequency (%)
2132977
25.0%
0132977
25.0%
1132977
25.0%
9132977
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common531908
100.0%

Most frequent character per script

ValueCountFrequency (%)
2132977
25.0%
0132977
25.0%
1132977
25.0%
9132977
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII531908
100.0%

Most frequent character per block

ValueCountFrequency (%)
2132977
25.0%
0132977
25.0%
1132977
25.0%
9132977
25.0%

hrmn
Categorical

HIGH CARDINALITY

Distinct1386
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
18:00
 
1870
17:30
 
1646
18:30
 
1628
17:00
 
1503
16:00
 
1420
Other values (1381)
124910 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters664885
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)< 0.1%

Sample

1st row01:30
2nd row01:30
3rd row01:30
4th row02:50
5th row15:15
ValueCountFrequency (%)
18:001870
 
1.4%
17:301646
 
1.2%
18:301628
 
1.2%
17:001503
 
1.1%
16:001420
 
1.1%
19:001409
 
1.1%
16:301235
 
0.9%
15:001202
 
0.9%
19:301095
 
0.8%
15:301089
 
0.8%
Other values (1376)118880
89.4%
2021-03-18T15:56:32.431391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:001870
 
1.4%
17:301646
 
1.2%
18:301628
 
1.2%
17:001503
 
1.1%
16:001420
 
1.1%
19:001409
 
1.1%
16:301235
 
0.9%
15:001202
 
0.9%
19:301095
 
0.8%
15:301089
 
0.8%
Other values (1376)118880
89.4%

Most occurring characters

ValueCountFrequency (%)
0150155
22.6%
:132977
20.0%
1118713
17.9%
571098
10.7%
248509
 
7.3%
338186
 
5.7%
433547
 
5.0%
821419
 
3.2%
719443
 
2.9%
916656
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number531908
80.0%
Other Punctuation132977
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0150155
28.2%
1118713
22.3%
571098
13.4%
248509
 
9.1%
338186
 
7.2%
433547
 
6.3%
821419
 
4.0%
719443
 
3.7%
916656
 
3.1%
614182
 
2.7%
ValueCountFrequency (%)
:132977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common664885
100.0%

Most frequent character per script

ValueCountFrequency (%)
0150155
22.6%
:132977
20.0%
1118713
17.9%
571098
10.7%
248509
 
7.3%
338186
 
5.7%
433547
 
5.0%
821419
 
3.2%
719443
 
2.9%
916656
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII664885
100.0%

Most frequent character per block

ValueCountFrequency (%)
0150155
22.6%
:132977
20.0%
1118713
17.9%
571098
10.7%
248509
 
7.3%
338186
 
5.7%
433547
 
5.0%
821419
 
3.2%
719443
 
2.9%
916656
 
2.5%

lum
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
88621 
5
21197 
3
13674 
2
 
8281
4
 
1204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132977
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row1
ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%
2021-03-18T15:56:32.584514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:32.638222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%

Most occurring characters

ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
100.0%

Most frequent character per category

ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common132977
100.0%

Most frequent character per script

ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII132977
100.0%

Most frequent character per block

ValueCountFrequency (%)
188621
66.6%
521197
 
15.9%
313674
 
10.3%
28281
 
6.2%
41204
 
0.9%

dep
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
75
11895 
93
 
7109
13
 
7003
94
 
6045
69
 
5782
Other values (102)
95143 

Length

Max length3
Median length2
Mean length1.999924799
Min length1

Characters and Unicode

Total characters265944
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row93
2nd row93
3rd row93
4th row93
5th row92
ValueCountFrequency (%)
7511895
 
8.9%
937109
 
5.3%
137003
 
5.3%
946045
 
4.5%
695782
 
4.3%
925304
 
4.0%
333530
 
2.7%
913384
 
2.5%
772455
 
1.8%
62341
 
1.8%
Other values (97)78129
58.8%
2021-03-18T15:56:32.844220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7511895
 
8.9%
937109
 
5.3%
137003
 
5.3%
946045
 
4.5%
695782
 
4.3%
925304
 
4.0%
333530
 
2.7%
913384
 
2.5%
772455
 
1.8%
62341
 
1.8%
Other values (97)78129
58.8%

Most occurring characters

ValueCountFrequency (%)
943235
16.3%
738746
14.6%
337500
14.1%
528084
10.6%
425614
9.6%
125301
9.5%
623902
9.0%
220292
7.6%
816053
 
6.0%
05778
 
2.2%
Other values (2)1439
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number264505
99.5%
Uppercase Letter1439
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
943235
16.3%
738746
14.6%
337500
14.2%
528084
10.6%
425614
9.7%
125301
9.6%
623902
9.0%
220292
7.7%
816053
 
6.1%
05778
 
2.2%
ValueCountFrequency (%)
B807
56.1%
A632
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common264505
99.5%
Latin1439
 
0.5%

Most frequent character per script

ValueCountFrequency (%)
943235
16.3%
738746
14.6%
337500
14.2%
528084
10.6%
425614
9.7%
125301
9.6%
623902
9.0%
220292
7.7%
816053
 
6.1%
05778
 
2.2%
ValueCountFrequency (%)
B807
56.1%
A632
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII265944
100.0%

Most frequent character per block

ValueCountFrequency (%)
943235
16.3%
738746
14.6%
337500
14.1%
528084
10.6%
425614
9.6%
125301
9.5%
623902
9.0%
220292
7.6%
816053
 
6.0%
05778
 
2.2%
Other values (2)1439
 
0.5%

com
Categorical

HIGH CARDINALITY

Distinct11444
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
75116
 
1418
75112
 
976
31555
 
952
75117
 
915
34172
 
882
Other values (11439)
127834 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters664885
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2006 ?
Unique (%)1.5%

Sample

1st row93053
2nd row93053
3rd row93053
4th row93066
5th row92036
ValueCountFrequency (%)
751161418
 
1.1%
75112976
 
0.7%
31555952
 
0.7%
75117915
 
0.7%
34172882
 
0.7%
75119855
 
0.6%
75115838
 
0.6%
67482824
 
0.6%
75120817
 
0.6%
35238811
 
0.6%
Other values (11434)123689
93.0%
2021-03-18T15:56:33.066451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
751161418
 
1.1%
75112976
 
0.7%
31555952
 
0.7%
75117915
 
0.7%
34172882
 
0.7%
75119855
 
0.6%
75115838
 
0.6%
67482824
 
0.6%
75120817
 
0.6%
35238811
 
0.6%
Other values (11434)123689
93.0%

Most occurring characters

ValueCountFrequency (%)
196397
14.5%
087580
13.2%
374199
11.2%
269430
10.4%
963582
9.6%
762725
9.4%
558245
8.8%
455806
8.4%
651717
7.8%
843765
6.6%
Other values (2)1439
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number663446
99.8%
Uppercase Letter1439
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
196397
14.5%
087580
13.2%
374199
11.2%
269430
10.5%
963582
9.6%
762725
9.5%
558245
8.8%
455806
8.4%
651717
7.8%
843765
6.6%
ValueCountFrequency (%)
B807
56.1%
A632
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common663446
99.8%
Latin1439
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
196397
14.5%
087580
13.2%
374199
11.2%
269430
10.5%
963582
9.6%
762725
9.5%
558245
8.8%
455806
8.4%
651717
7.8%
843765
6.6%
ValueCountFrequency (%)
B807
56.1%
A632
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII664885
100.0%

Most frequent character per block

ValueCountFrequency (%)
196397
14.5%
087580
13.2%
374199
11.2%
269430
10.4%
963582
9.6%
762725
9.4%
558245
8.8%
455806
8.4%
651717
7.8%
843765
6.6%
Other values (2)1439
 
0.2%

agg
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2
82791 
1
50186 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132977
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
282791
62.3%
150186
37.7%
2021-03-18T15:56:33.226216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:33.277869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
282791
62.3%
150186
37.7%

Most occurring characters

ValueCountFrequency (%)
282791
62.3%
150186
37.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
100.0%

Most frequent character per category

ValueCountFrequency (%)
282791
62.3%
150186
37.7%

Most occurring scripts

ValueCountFrequency (%)
Common132977
100.0%

Most frequent character per script

ValueCountFrequency (%)
282791
62.3%
150186
37.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII132977
100.0%

Most frequent character per block

ValueCountFrequency (%)
282791
62.3%
150186
37.7%

int
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016551735
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:33.326399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.98270514
Coefficient of variation (CV)0.9832156082
Kurtosis5.035846187
Mean2.016551735
Median Absolute Deviation (MAD)0
Skewness2.389790744
Sum268155
Variance3.931119673
MonotocityNot monotonic
2021-03-18T15:56:33.397803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
187068
65.5%
216547
 
12.4%
313769
 
10.4%
95916
 
4.4%
64504
 
3.4%
42859
 
2.1%
71487
 
1.1%
5681
 
0.5%
8146
 
0.1%
ValueCountFrequency (%)
187068
65.5%
216547
 
12.4%
313769
 
10.4%
42859
 
2.1%
5681
 
0.5%
ValueCountFrequency (%)
95916
4.4%
8146
 
0.1%
71487
 
1.1%
64504
3.4%
5681
 
0.5%

atm
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.614279161
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:33.473627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.672984771
Coefficient of variation (CV)1.036366455
Kurtosis8.919823271
Mean1.614279161
Median Absolute Deviation (MAD)0
Skewness3.162367382
Sum214662
Variance2.798878044
MonotocityNot monotonic
2021-03-18T15:56:33.543134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1105340
79.2%
214749
 
11.1%
84944
 
3.7%
33244
 
2.4%
72336
 
1.8%
5723
 
0.5%
9656
 
0.5%
4617
 
0.5%
6367
 
0.3%
-11
 
< 0.1%
ValueCountFrequency (%)
-11
 
< 0.1%
1105340
79.2%
214749
 
11.1%
33244
 
2.4%
4617
 
0.5%
ValueCountFrequency (%)
9656
 
0.5%
84944
3.7%
72336
1.8%
6367
 
0.3%
5723
 
0.5%

col
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.890071215
Minimum-1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:33.611656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median3
Q36
95-th percentile7
Maximum7
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.862988057
Coefficient of variation (CV)0.4789084707
Kurtosis-1.288004359
Mean3.890071215
Median Absolute Deviation (MAD)1
Skewness0.1298636705
Sum517290
Variance3.470724499
MonotocityNot monotonic
2021-03-18T15:56:33.686539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
340350
30.3%
634838
26.2%
217977
13.5%
113956
 
10.5%
49292
 
7.0%
78663
 
6.5%
57899
 
5.9%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
113956
 
10.5%
217977
13.5%
340350
30.3%
49292
 
7.0%
ValueCountFrequency (%)
78663
 
6.5%
634838
26.2%
57899
 
5.9%
49292
 
7.0%
340350
30.3%

adr
Categorical

HIGH CARDINALITY

Distinct32020
Distinct (%)24.3%
Missing1021
Missing (%)0.8%
Memory size2.0 MiB
AUTOROUTE A86
 
808
A4
 
786
AUTOROUTE A1
 
781
AUTOROUTE A3
 
677
A13
 
608
Other values (32015)
128296 

Length

Max length158
Median length18
Mean length18.46760284
Min length1

Characters and Unicode

Total characters2436911
Distinct characters107
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5426 ?
Unique (%)4.1%

Sample

1st rowAUTOROUTE A3
2nd rowAUTOROUTE A3
3rd rowAUTOROUTE A3
4th rowAUTOROUTE A1
5th rowAUTOROUTE A86
ValueCountFrequency (%)
AUTOROUTE A86808
 
0.6%
A4786
 
0.6%
AUTOROUTE A1781
 
0.6%
AUTOROUTE A3677
 
0.5%
A13608
 
0.5%
AUTOROUTE A15542
 
0.4%
AUTOROUTE A6529
 
0.4%
RN 104453
 
0.3%
ROCADE A 630373
 
0.3%
A86359
 
0.3%
Other values (32010)126040
94.8%
(Missing)1021
 
0.8%
2021-03-18T15:56:33.988062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de32696
 
7.8%
rue24183
 
5.8%
avenue15443
 
3.7%
du11618
 
2.8%
route11346
 
2.7%
11269
 
2.7%
la9983
 
2.4%
boulevard7787
 
1.9%
autoroute6584
 
1.6%
des6184
 
1.5%
Other values (21309)281081
67.2%

Most occurring characters

ValueCountFrequency (%)
373620
 
15.3%
E212983
 
8.7%
A145848
 
6.0%
R139373
 
5.7%
U104300
 
4.3%
D87873
 
3.6%
e84045
 
3.4%
N80883
 
3.3%
L76787
 
3.2%
O71385
 
2.9%
Other values (97)1059814
43.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1345080
55.2%
Lowercase Letter438170
 
18.0%
Space Separator373620
 
15.3%
Decimal Number153522
 
6.3%
Open Punctuation47744
 
2.0%
Close Punctuation47018
 
1.9%
Other Punctuation16456
 
0.7%
Dash Punctuation10648
 
0.4%
Math Symbol3052
 
0.1%
Other Symbol1574
 
0.1%
Other values (2)27
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e84045
19.2%
u37989
8.7%
a36438
8.3%
r34228
7.8%
n30291
 
6.9%
o29166
 
6.7%
t28540
 
6.5%
l25349
 
5.8%
i24845
 
5.7%
d24694
 
5.6%
Other values (28)82585
18.8%
ValueCountFrequency (%)
E212983
15.8%
A145848
10.8%
R139373
10.4%
U104300
 
7.8%
D87873
 
6.5%
N80883
 
6.0%
L76787
 
5.7%
O71385
 
5.3%
I61711
 
4.6%
T61119
 
4.5%
Other values (26)302818
22.5%
ValueCountFrequency (%)
130107
19.6%
023403
15.2%
215343
10.0%
314465
9.4%
613722
8.9%
413205
8.6%
512968
8.4%
810583
 
6.9%
99976
 
6.5%
79750
 
6.4%
ValueCountFrequency (%)
.6260
38.0%
'4723
28.7%
/2849
17.3%
,1798
 
10.9%
"648
 
3.9%
:160
 
1.0%
?8
 
< 0.1%
;7
 
< 0.1%
&3
 
< 0.1%
ValueCountFrequency (%)
+2702
88.5%
>203
 
6.7%
=145
 
4.8%
<2
 
0.1%
ValueCountFrequency (%)
(47686
99.9%
[58
 
0.1%
ValueCountFrequency (%)
)46960
99.9%
]58
 
0.1%
ValueCountFrequency (%)
°1570
99.7%
©4
 
0.3%
ValueCountFrequency (%)
373620
100.0%
ValueCountFrequency (%)
-10648
100.0%
ValueCountFrequency (%)
_25
100.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1783250
73.2%
Common653661
 
26.8%

Most frequent character per script

ValueCountFrequency (%)
E212983
 
11.9%
A145848
 
8.2%
R139373
 
7.8%
U104300
 
5.8%
D87873
 
4.9%
e84045
 
4.7%
N80883
 
4.5%
L76787
 
4.3%
O71385
 
4.0%
I61711
 
3.5%
Other values (64)718062
40.3%
ValueCountFrequency (%)
373620
57.2%
(47686
 
7.3%
)46960
 
7.2%
130107
 
4.6%
023403
 
3.6%
215343
 
2.3%
314465
 
2.2%
613722
 
2.1%
413205
 
2.0%
512968
 
2.0%
Other values (23)62182
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2427458
99.6%
None9451
 
0.4%
Punctuation2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
373620
 
15.4%
E212983
 
8.8%
A145848
 
6.0%
R139373
 
5.7%
U104300
 
4.3%
D87873
 
3.6%
e84045
 
3.5%
N80883
 
3.3%
L76787
 
3.2%
O71385
 
2.9%
Other values (72)1050361
43.3%
ValueCountFrequency (%)
é5268
55.7%
°1570
 
16.6%
è1259
 
13.3%
É578
 
6.1%
â139
 
1.5%
à127
 
1.3%
ç108
 
1.1%
ê79
 
0.8%
ô75
 
0.8%
È57
 
0.6%
Other values (14)191
 
2.0%
ValueCountFrequency (%)
2
100.0%

lat
Categorical

HIGH CARDINALITY
UNIFORM

Distinct55671
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
48,8100000
 
48
43,1213200
 
47
48,7900000
 
45
43,1334900
 
33
49,8784070
 
33
Other values (55666)
132771 

Length

Max length12
Median length10
Mean length10.04498522
Min length9

Characters and Unicode

Total characters1335752
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10714 ?
Unique (%)8.1%

Sample

1st row48,8962100
2nd row48,8962100
3rd row48,8962100
4th row48,9307000
5th row48,9358718
ValueCountFrequency (%)
48,810000048
 
< 0.1%
43,121320047
 
< 0.1%
48,790000045
 
< 0.1%
43,133490033
 
< 0.1%
49,878407033
 
< 0.1%
48,910000033
 
< 0.1%
43,157885030
 
< 0.1%
48,820000028
 
< 0.1%
43,143400028
 
< 0.1%
48,924400025
 
< 0.1%
Other values (55661)132627
99.7%
2021-03-18T15:56:34.303848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
48,810000048
 
< 0.1%
43,121320047
 
< 0.1%
48,790000045
 
< 0.1%
48,910000033
 
< 0.1%
49,878407033
 
< 0.1%
43,133490033
 
< 0.1%
43,157885030
 
< 0.1%
43,143400028
 
< 0.1%
48,820000028
 
< 0.1%
48,800000025
 
< 0.1%
Other values (55661)132627
99.7%

Most occurring characters

ValueCountFrequency (%)
0254627
19.1%
4202863
15.2%
8142103
10.6%
,132977
10.0%
392786
 
6.9%
790335
 
6.8%
989073
 
6.7%
588778
 
6.6%
683521
 
6.3%
278267
 
5.9%
Other values (3)80422
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1195843
89.5%
Other Punctuation132977
 
10.0%
Space Separator3466
 
0.3%
Dash Punctuation3466
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
0254627
21.3%
4202863
17.0%
8142103
11.9%
392786
 
7.8%
790335
 
7.6%
989073
 
7.4%
588778
 
7.4%
683521
 
7.0%
278267
 
6.5%
173490
 
6.1%
ValueCountFrequency (%)
,132977
100.0%
ValueCountFrequency (%)
3466
100.0%
ValueCountFrequency (%)
-3466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1335752
100.0%

Most frequent character per script

ValueCountFrequency (%)
0254627
19.1%
4202863
15.2%
8142103
10.6%
,132977
10.0%
392786
 
6.9%
790335
 
6.8%
989073
 
6.7%
588778
 
6.6%
683521
 
6.3%
278267
 
5.9%
Other values (3)80422
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1335752
100.0%

Most frequent character per block

ValueCountFrequency (%)
0254627
19.1%
4202863
15.2%
8142103
10.6%
,132977
10.0%
392786
 
6.9%
790335
 
6.8%
989073
 
6.7%
588778
 
6.6%
683521
 
6.3%
278267
 
5.9%
Other values (3)80422
 
6.0%

long
Categorical

HIGH CARDINALITY
UNIFORM

Distinct56211
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
5,9533100
 
47
2,4900000
 
35
5,9847600
 
33
2,8373390
 
33
2,8563670
 
30
Other values (56206)
132799 

Length

Max length13
Median length9
Mean length9.400610632
Min length9

Characters and Unicode

Total characters1250065
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10855 ?
Unique (%)8.2%

Sample

1st row2,4701200
2nd row2,4701200
3rd row2,4701200
4th row2,3688000
5th row2,3191744
ValueCountFrequency (%)
5,953310047
 
< 0.1%
2,490000035
 
< 0.1%
5,984760033
 
< 0.1%
2,837339033
 
< 0.1%
2,856367030
 
< 0.1%
2,440000030
 
< 0.1%
5,400000028
 
< 0.1%
6,014200028
 
< 0.1%
2,346760027
 
< 0.1%
2,263000026
 
< 0.1%
Other values (56201)132660
99.8%
2021-03-18T15:56:34.599441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,953310047
 
< 0.1%
2,490000035
 
< 0.1%
2,837339033
 
< 0.1%
5,984760033
 
< 0.1%
2,856367030
 
< 0.1%
2,440000030
 
< 0.1%
5,400000028
 
< 0.1%
6,014200028
 
< 0.1%
2,346760027
 
< 0.1%
2,263000026
 
< 0.1%
Other values (56084)132660
99.8%

Most occurring characters

ValueCountFrequency (%)
0268381
21.5%
,132977
10.6%
2125342
10.0%
496983
 
7.8%
396138
 
7.7%
592156
 
7.4%
191019
 
7.3%
681714
 
6.5%
775466
 
6.0%
873704
 
5.9%
Other values (3)116185
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1071612
85.7%
Other Punctuation132977
 
10.6%
Space Separator22738
 
1.8%
Dash Punctuation22738
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
0268381
25.0%
2125342
11.7%
496983
 
9.1%
396138
 
9.0%
592156
 
8.6%
191019
 
8.5%
681714
 
7.6%
775466
 
7.0%
873704
 
6.9%
970709
 
6.6%
ValueCountFrequency (%)
,132977
100.0%
ValueCountFrequency (%)
22738
100.0%
ValueCountFrequency (%)
-22738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1250065
100.0%

Most frequent character per script

ValueCountFrequency (%)
0268381
21.5%
,132977
10.6%
2125342
10.0%
496983
 
7.8%
396138
 
7.7%
592156
 
7.4%
191019
 
7.3%
681714
 
6.5%
775466
 
6.0%
873704
 
5.9%
Other values (3)116185
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250065
100.0%

Most frequent character per block

ValueCountFrequency (%)
0268381
21.5%
,132977
10.6%
2125342
10.0%
496983
 
7.8%
396138
 
7.7%
592156
 
7.4%
191019
 
7.3%
681714
 
6.5%
775466
 
6.0%
873704
 
5.9%
Other values (3)116185
9.3%

catr
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.270197102
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:34.685126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.28271712
Coefficient of variation (CV)0.3922445895
Kurtosis3.686818671
Mean3.270197102
Median Absolute Deviation (MAD)1
Skewness0.7745566859
Sum434861
Variance1.645363209
MonotocityNot monotonic
2021-03-18T15:56:34.756469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
455157
41.5%
347061
35.4%
115915
 
12.0%
29595
 
7.2%
72929
 
2.2%
91228
 
0.9%
6930
 
0.7%
5162
 
0.1%
ValueCountFrequency (%)
115915
 
12.0%
29595
 
7.2%
347061
35.4%
455157
41.5%
5162
 
0.1%
ValueCountFrequency (%)
91228
 
0.9%
72929
 
2.2%
6930
 
0.7%
5162
 
0.1%
455157
41.5%

voie
Categorical

HIGH CARDINALITY
MISSING

Distinct14492
Distinct (%)11.4%
Missing5914
Missing (%)4.4%
Memory size2.0 MiB
1
 
2750
7
 
2091
86
 
2081
6
 
2079
4
 
1976
Other values (14487)
116086 

Length

Max length51
Median length3
Mean length10.11544667
Min length1

Characters and Unicode

Total characters1285299
Distinct characters98
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1713 ?
Unique (%)1.3%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row86
ValueCountFrequency (%)
12750
 
2.1%
72091
 
1.6%
862081
 
1.6%
62079
 
1.6%
41976
 
1.5%
31591
 
1.2%
101389
 
1.0%
21294
 
1.0%
131185
 
0.9%
1041128
 
0.8%
Other values (14482)109499
82.3%
(Missing)5914
 
4.4%
2021-03-18T15:56:35.007422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue15665
 
6.3%
de15278
 
6.2%
avenue8756
 
3.5%
du6056
 
2.4%
boulevard5540
 
2.2%
la4569
 
1.8%
4372
 
1.8%
des3330
 
1.3%
13028
 
1.2%
bd3018
 
1.2%
Other values (9840)178156
71.9%

Most occurring characters

ValueCountFrequency (%)
201263
15.7%
E140325
 
10.9%
R78845
 
6.1%
A76192
 
5.9%
U64619
 
5.0%
N47944
 
3.7%
L47164
 
3.7%
D46418
 
3.6%
I42463
 
3.3%
O37817
 
2.9%
Other values (88)502249
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter794500
61.8%
Space Separator201263
 
15.7%
Decimal Number185969
 
14.5%
Lowercase Letter36002
 
2.8%
Open Punctuation30760
 
2.4%
Close Punctuation30317
 
2.4%
Other Punctuation3858
 
0.3%
Dash Punctuation1882
 
0.1%
Other Symbol648
 
0.1%
Math Symbol97
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e6796
18.9%
u3482
9.7%
a3099
8.6%
r2803
 
7.8%
n2607
 
7.2%
d2483
 
6.9%
l2042
 
5.7%
o2012
 
5.6%
i1909
 
5.3%
t1539
 
4.3%
Other values (26)7230
20.1%
ValueCountFrequency (%)
E140325
17.7%
R78845
9.9%
A76192
9.6%
U64619
 
8.1%
N47944
 
6.0%
L47164
 
5.9%
D46418
 
5.8%
I42463
 
5.3%
O37817
 
4.8%
S31120
 
3.9%
Other values (21)181593
22.9%
ValueCountFrequency (%)
137133
20.0%
020218
10.9%
218749
10.1%
317969
9.7%
617512
9.4%
917198
9.2%
415806
8.5%
515103
8.1%
813505
 
7.3%
712776
 
6.9%
ValueCountFrequency (%)
'2007
52.0%
.1288
33.4%
/274
 
7.1%
"143
 
3.7%
,98
 
2.5%
:30
 
0.8%
*12
 
0.3%
%2
 
0.1%
;2
 
0.1%
#2
 
0.1%
ValueCountFrequency (%)
+42
43.3%
>39
40.2%
=16
 
16.5%
ValueCountFrequency (%)
(30752
> 99.9%
[8
 
< 0.1%
ValueCountFrequency (%)
)30309
> 99.9%
]8
 
< 0.1%
ValueCountFrequency (%)
201263
100.0%
ValueCountFrequency (%)
-1882
100.0%
ValueCountFrequency (%)
°648
100.0%
ValueCountFrequency (%)
_3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin830502
64.6%
Common454797
35.4%

Most frequent character per script

ValueCountFrequency (%)
E140325
16.9%
R78845
 
9.5%
A76192
 
9.2%
U64619
 
7.8%
N47944
 
5.8%
L47164
 
5.7%
D46418
 
5.6%
I42463
 
5.1%
O37817
 
4.6%
S31120
 
3.7%
Other values (57)217595
26.2%
ValueCountFrequency (%)
201263
44.3%
137133
 
8.2%
(30752
 
6.8%
)30309
 
6.7%
020218
 
4.4%
218749
 
4.1%
317969
 
4.0%
617512
 
3.9%
917198
 
3.8%
415806
 
3.5%
Other values (21)47888
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1283601
99.9%
None1698
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
201263
15.7%
E140325
 
10.9%
R78845
 
6.1%
A76192
 
5.9%
U64619
 
5.0%
N47944
 
3.7%
L47164
 
3.7%
D46418
 
3.6%
I42463
 
3.3%
O37817
 
2.9%
Other values (72)500551
39.0%
ValueCountFrequency (%)
°648
38.2%
é582
34.3%
É207
 
12.2%
è93
 
5.5%
Ç41
 
2.4%
ç40
 
2.4%
à20
 
1.2%
È20
 
1.2%
ø15
 
0.9%
ê9
 
0.5%
Other values (6)23
 
1.4%

v1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0.0
107905 
nan
24596 
2.0
 
416
3.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters398931
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0107905
81.1%
nan24596
 
18.5%
2.0416
 
0.3%
3.060
 
< 0.1%
2021-03-18T15:56:35.188589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:35.242961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0107905
81.1%
nan24596
 
18.5%
2.0416
 
0.3%
3.060
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0216286
54.2%
.108381
27.2%
n49192
 
12.3%
a24596
 
6.2%
2416
 
0.1%
360
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number216762
54.3%
Other Punctuation108381
27.2%
Lowercase Letter73788
 
18.5%

Most frequent character per category

ValueCountFrequency (%)
0216286
99.8%
2416
 
0.2%
360
 
< 0.1%
ValueCountFrequency (%)
n49192
66.7%
a24596
33.3%
ValueCountFrequency (%)
.108381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common325143
81.5%
Latin73788
 
18.5%

Most frequent character per script

ValueCountFrequency (%)
0216286
66.5%
.108381
33.3%
2416
 
0.1%
360
 
< 0.1%
ValueCountFrequency (%)
n49192
66.7%
a24596
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII398931
100.0%

Most frequent character per block

ValueCountFrequency (%)
0216286
54.2%
.108381
27.2%
n49192
 
12.3%
a24596
 
6.2%
2416
 
0.1%
360
 
< 0.1%

v2
Categorical

HIGH CORRELATION
MISSING

Distinct35
Distinct (%)0.4%
Missing123235
Missing (%)92.7%
Memory size2.0 MiB
D
4082 
A
2377 
B
572 
N
543 
E
543 
Other values (30)
1625 

Length

Max length3
Median length1
Mean length1.128618354
Min length1

Characters and Unicode

Total characters10995
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowINT
2nd rowINT
3rd rowINT
4th rowB
5th rowB
ValueCountFrequency (%)
D4082
 
3.1%
A2377
 
1.8%
B572
 
0.4%
N543
 
0.4%
E543
 
0.4%
INT307
 
0.2%
EXT248
 
0.2%
C232
 
0.2%
R160
 
0.1%
-128
 
0.1%
Other values (25)550
 
0.4%
(Missing)123235
92.7%
2021-03-18T15:56:35.429008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d4083
41.9%
a2377
24.4%
b592
 
6.1%
n543
 
5.6%
e543
 
5.6%
int307
 
3.2%
ext248
 
2.5%
c232
 
2.4%
r160
 
1.6%
128
 
1.3%
Other values (23)529
 
5.4%

Most occurring characters

ValueCountFrequency (%)
D4085
37.2%
A2380
21.6%
N850
 
7.7%
E791
 
7.2%
T620
 
5.6%
B572
 
5.2%
I330
 
3.0%
X257
 
2.3%
C234
 
2.1%
R160
 
1.5%
Other values (19)716
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10713
97.4%
Space Separator129
 
1.2%
Dash Punctuation128
 
1.2%
Lowercase Letter20
 
0.2%
Decimal Number5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
D4085
38.1%
A2380
22.2%
N850
 
7.9%
E791
 
7.4%
T620
 
5.8%
B572
 
5.3%
I330
 
3.1%
X257
 
2.4%
C234
 
2.2%
R160
 
1.5%
Other values (14)434
 
4.1%
ValueCountFrequency (%)
14
80.0%
51
 
20.0%
ValueCountFrequency (%)
129
100.0%
ValueCountFrequency (%)
-128
100.0%
ValueCountFrequency (%)
b20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10733
97.6%
Common262
 
2.4%

Most frequent character per script

ValueCountFrequency (%)
D4085
38.1%
A2380
22.2%
N850
 
7.9%
E791
 
7.4%
T620
 
5.8%
B572
 
5.3%
I330
 
3.1%
X257
 
2.4%
C234
 
2.2%
R160
 
1.5%
Other values (15)454
 
4.2%
ValueCountFrequency (%)
129
49.2%
-128
48.9%
14
 
1.5%
51
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10995
100.0%

Most frequent character per block

ValueCountFrequency (%)
D4085
37.2%
A2380
21.6%
N850
 
7.7%
E791
 
7.2%
T620
 
5.6%
B572
 
5.2%
I330
 
3.0%
X257
 
2.3%
C234
 
2.1%
R160
 
1.5%
Other values (19)716
 
6.5%

circ
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2
80818 
1
23094 
3
21502 
-1
 
6726
4
 
837

Length

Max length2
Median length1
Mean length1.050580176
Min length1

Characters and Unicode

Total characters139703
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row3
ValueCountFrequency (%)
280818
60.8%
123094
 
17.4%
321502
 
16.2%
-16726
 
5.1%
4837
 
0.6%
2021-03-18T15:56:35.612857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:35.669054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
280818
60.8%
129820
 
22.4%
321502
 
16.2%
4837
 
0.6%

Most occurring characters

ValueCountFrequency (%)
280818
57.8%
129820
 
21.3%
321502
 
15.4%
-6726
 
4.8%
4837
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
95.2%
Dash Punctuation6726
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
280818
60.8%
129820
 
22.4%
321502
 
16.2%
4837
 
0.6%
ValueCountFrequency (%)
-6726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common139703
100.0%

Most frequent character per script

ValueCountFrequency (%)
280818
57.8%
129820
 
21.3%
321502
 
15.4%
-6726
 
4.8%
4837
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII139703
100.0%

Most frequent character per block

ValueCountFrequency (%)
280818
57.8%
129820
 
21.3%
321502
 
15.4%
-6726
 
4.8%
4837
 
0.6%

nbv
Real number (ℝ)

ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.479090369
Minimum-1
Maximum12
Zeros3476
Zeros (%)2.6%
Memory size2.0 MiB
2021-03-18T15:56:35.733962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum12
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.485557225
Coefficient of variation (CV)0.5992348016
Kurtosis5.734972586
Mean2.479090369
Median Absolute Deviation (MAD)0
Skewness1.782176507
Sum329662
Variance2.20688027
MonotocityNot monotonic
2021-03-18T15:56:35.819380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
278819
59.3%
417072
 
12.8%
111938
 
9.0%
310805
 
8.1%
64084
 
3.1%
03476
 
2.6%
52635
 
2.0%
-11406
 
1.1%
81337
 
1.0%
7531
 
0.4%
Other values (4)874
 
0.7%
ValueCountFrequency (%)
-11406
 
1.1%
03476
 
2.6%
111938
 
9.0%
278819
59.3%
310805
 
8.1%
ValueCountFrequency (%)
1275
 
0.1%
1194
 
0.1%
10418
 
0.3%
9287
 
0.2%
81337
1.0%

vosp
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
121681 
1
 
4085
3
 
3891
2
 
1890
-1
 
1430

Length

Max length2
Median length1
Mean length1.010753739
Min length1

Characters and Unicode

Total characters134407
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0121681
91.5%
14085
 
3.1%
33891
 
2.9%
21890
 
1.4%
-11430
 
1.1%
2021-03-18T15:56:35.997056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:36.053188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0121681
91.5%
15515
 
4.1%
33891
 
2.9%
21890
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0121681
90.5%
15515
 
4.1%
33891
 
2.9%
21890
 
1.4%
-1430
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
98.9%
Dash Punctuation1430
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
0121681
91.5%
15515
 
4.1%
33891
 
2.9%
21890
 
1.4%
ValueCountFrequency (%)
-1430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common134407
100.0%

Most frequent character per script

ValueCountFrequency (%)
0121681
90.5%
15515
 
4.1%
33891
 
2.9%
21890
 
1.4%
-1430
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII134407
100.0%

Most frequent character per block

ValueCountFrequency (%)
0121681
90.5%
15515
 
4.1%
33891
 
2.9%
21890
 
1.4%
-1430
 
1.1%

prof
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
107390 
2
21131 
3
 
2328
4
 
2097
-1
 
31

Length

Max length2
Median length1
Mean length1.000233123
Min length1

Characters and Unicode

Total characters133008
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row4
5th row1
ValueCountFrequency (%)
1107390
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%
-131
 
< 0.1%
2021-03-18T15:56:36.212070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:36.268490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1107421
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%

Most occurring characters

ValueCountFrequency (%)
1107421
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%
-31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
> 99.9%
Dash Punctuation31
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
1107421
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%
ValueCountFrequency (%)
-31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common133008
100.0%

Most frequent character per script

ValueCountFrequency (%)
1107421
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%
-31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII133008
100.0%

Most frequent character per block

ValueCountFrequency (%)
1107421
80.8%
221131
 
15.9%
32328
 
1.8%
42097
 
1.6%
-31
 
< 0.1%

pr
Categorical

HIGH CARDINALITY

Distinct489
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
58844 
(1)
15781 
1
7896 
2
 
2669
3
 
2173
Other values (484)
45614 

Length

Max length4
Median length1
Mean length1.54401137
Min length1

Characters and Unicode

Total characters205318
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st row6
2nd row6
3rd row6
4th row3
5th row10
ValueCountFrequency (%)
058844
44.3%
(1)15781
 
11.9%
17896
 
5.9%
22669
 
2.0%
32173
 
1.6%
42057
 
1.5%
61902
 
1.4%
51872
 
1.4%
71561
 
1.2%
81406
 
1.1%
Other values (479)36816
27.7%
2021-03-18T15:56:36.464906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
058844
44.3%
123677
17.8%
22669
 
2.0%
32173
 
1.6%
42057
 
1.5%
61902
 
1.4%
51872
 
1.4%
71561
 
1.2%
81406
 
1.1%
91379
 
1.0%
Other values (478)35437
26.6%

Most occurring characters

ValueCountFrequency (%)
063874
31.1%
141086
20.0%
(15781
 
7.7%
)15781
 
7.7%
214951
 
7.3%
311757
 
5.7%
49671
 
4.7%
58193
 
4.0%
67064
 
3.4%
76369
 
3.1%
Other values (2)10791
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number173756
84.6%
Open Punctuation15781
 
7.7%
Close Punctuation15781
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
063874
36.8%
141086
23.6%
214951
 
8.6%
311757
 
6.8%
49671
 
5.6%
58193
 
4.7%
67064
 
4.1%
76369
 
3.7%
85448
 
3.1%
95343
 
3.1%
ValueCountFrequency (%)
(15781
100.0%
ValueCountFrequency (%)
)15781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common205318
100.0%

Most frequent character per script

ValueCountFrequency (%)
063874
31.1%
141086
20.0%
(15781
 
7.7%
)15781
 
7.7%
214951
 
7.3%
311757
 
5.7%
49671
 
4.7%
58193
 
4.0%
67064
 
3.4%
76369
 
3.1%
Other values (2)10791
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII205318
100.0%

Most frequent character per block

ValueCountFrequency (%)
063874
31.1%
141086
20.0%
(15781
 
7.7%
)15781
 
7.7%
214951
 
7.3%
311757
 
5.7%
49671
 
4.7%
58193
 
4.0%
67064
 
3.4%
76369
 
3.1%
Other values (2)10791
 
5.3%

pr1
Categorical

HIGH CARDINALITY

Distinct1103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
66522 
(1)
16461 
500
 
4963
200
 
2748
100
 
2624
Other values (1098)
39659 

Length

Max length4
Median length1
Mean length1.939004489
Min length1

Characters and Unicode

Total characters257843
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row900
2nd row900
3rd row900
4th row845
5th row500
ValueCountFrequency (%)
066522
50.0%
(1)16461
 
12.4%
5004963
 
3.7%
2002748
 
2.1%
1002624
 
2.0%
8002604
 
2.0%
12357
 
1.8%
6002201
 
1.7%
4002181
 
1.6%
3002025
 
1.5%
Other values (1093)28291
21.3%
2021-03-18T15:56:36.676381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
066522
50.0%
118818
 
14.2%
5004963
 
3.7%
2002748
 
2.1%
1002624
 
2.0%
8002604
 
2.0%
6002201
 
1.7%
4002181
 
1.6%
3002025
 
1.5%
7001945
 
1.5%
Other values (1092)26346
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0125467
48.7%
126856
 
10.4%
(16461
 
6.4%
)16461
 
6.4%
515848
 
6.1%
913069
 
5.1%
28442
 
3.3%
87702
 
3.0%
37252
 
2.8%
76809
 
2.6%
Other values (2)13476
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number224921
87.2%
Open Punctuation16461
 
6.4%
Close Punctuation16461
 
6.4%

Most frequent character per category

ValueCountFrequency (%)
0125467
55.8%
126856
 
11.9%
515848
 
7.0%
913069
 
5.8%
28442
 
3.8%
87702
 
3.4%
37252
 
3.2%
76809
 
3.0%
66768
 
3.0%
46708
 
3.0%
ValueCountFrequency (%)
(16461
100.0%
ValueCountFrequency (%)
)16461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common257843
100.0%

Most frequent character per script

ValueCountFrequency (%)
0125467
48.7%
126856
 
10.4%
(16461
 
6.4%
)16461
 
6.4%
515848
 
6.1%
913069
 
5.1%
28442
 
3.3%
87702
 
3.0%
37252
 
2.8%
76809
 
2.6%
Other values (2)13476
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII257843
100.0%

Most frequent character per block

ValueCountFrequency (%)
0125467
48.7%
126856
 
10.4%
(16461
 
6.4%
)16461
 
6.4%
515848
 
6.1%
913069
 
5.1%
28442
 
3.3%
87702
 
3.0%
37252
 
2.8%
76809
 
2.6%
Other values (2)13476
 
5.2%

plan
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
109073 
2
11207 
3
11064 
4
 
1617
-1
 
16

Length

Max length2
Median length1
Mean length1.000120322
Min length1

Characters and Unicode

Total characters132993
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row3
ValueCountFrequency (%)
1109073
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%
-116
 
< 0.1%
2021-03-18T15:56:36.847932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T15:56:36.904293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1109089
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%

Most occurring characters

ValueCountFrequency (%)
1109089
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%
-16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number132977
> 99.9%
Dash Punctuation16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
1109089
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%
ValueCountFrequency (%)
-16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common132993
100.0%

Most frequent character per script

ValueCountFrequency (%)
1109089
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%
-16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII132993
100.0%

Most frequent character per block

ValueCountFrequency (%)
1109089
82.0%
211207
 
8.4%
311064
 
8.3%
41617
 
1.2%
-16
 
< 0.1%

lartpc
Real number (ℝ≥0)

MISSING

Distinct26
Distinct (%)5.6%
Missing132510
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean6.760385438
Minimum0
Maximum180
Zeros312
Zeros (%)0.2%
Memory size2.0 MiB
2021-03-18T15:56:36.976411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile45
Maximum180
Range180
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation19.17557107
Coefficient of variation (CV)2.836461212
Kurtosis42.1200109
Mean6.760385438
Median Absolute Deviation (MAD)0
Skewness5.509732335
Sum3157.1
Variance367.702526
MonotocityNot monotonic
2021-03-18T15:56:37.063599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0312
 
0.2%
3026
 
< 0.1%
2.524
 
< 0.1%
5017
 
< 0.1%
216
 
< 0.1%
311
 
< 0.1%
18
 
< 0.1%
46
 
< 0.1%
206
 
< 0.1%
456
 
< 0.1%
Other values (16)35
 
< 0.1%
(Missing)132510
99.6%
ValueCountFrequency (%)
0312
0.2%
18
 
< 0.1%
1.53
 
< 0.1%
216
 
< 0.1%
2.524
 
< 0.1%
ValueCountFrequency (%)
1803
 
< 0.1%
603
 
< 0.1%
5017
< 0.1%
456
 
< 0.1%
351
 
< 0.1%

larrout
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct110
Distinct (%)13.2%
Missing132146
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean47.92635379
Minimum2.4
Maximum730
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:37.166565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile4.3
Q16.2
median14
Q367.5
95-th percentile145
Maximum730
Range727.6
Interquartile range (IQR)61.3

Descriptive statistics

Standard deviation72.83191755
Coefficient of variation (CV)1.519663229
Kurtosis49.08053943
Mean47.92635379
Median Absolute Deviation (MAD)11.1
Skewness5.833710557
Sum39826.8
Variance5304.488214
MonotocityNot monotonic
2021-03-18T15:56:37.272924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
782
 
0.1%
1440
 
< 0.1%
7027
 
< 0.1%
6526
 
< 0.1%
5025
 
< 0.1%
6021
 
< 0.1%
14021
 
< 0.1%
619
 
< 0.1%
5.618
 
< 0.1%
17018
 
< 0.1%
Other values (100)534
 
0.4%
(Missing)132146
99.4%
ValueCountFrequency (%)
2.45
< 0.1%
2.82
 
< 0.1%
2.95
< 0.1%
32
 
< 0.1%
3.12
 
< 0.1%
ValueCountFrequency (%)
7303
< 0.1%
7003
< 0.1%
2752
< 0.1%
1903
< 0.1%
1802
< 0.1%

surf
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.266030968
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:37.366879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8079890418
Coefficient of variation (CV)0.6382063807
Kurtosis52.77807618
Mean1.266030968
Median Absolute Deviation (MAD)0
Skewness6.481029463
Sum168353
Variance0.6528462917
MonotocityNot monotonic
2021-03-18T15:56:37.436961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1106451
80.1%
224647
 
18.5%
9614
 
0.5%
7434
 
0.3%
5255
 
0.2%
3235
 
0.2%
8196
 
0.1%
658
 
< 0.1%
447
 
< 0.1%
-140
 
< 0.1%
ValueCountFrequency (%)
-140
 
< 0.1%
1106451
80.1%
224647
 
18.5%
3235
 
0.2%
447
 
< 0.1%
ValueCountFrequency (%)
9614
0.5%
8196
 
0.1%
7434
0.3%
658
 
< 0.1%
5255
0.2%

infra
Real number (ℝ)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8746700557
Minimum-1
Maximum9
Zeros110523
Zeros (%)83.1%
Memory size2.0 MiB
2021-03-18T15:56:37.507196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.221849345
Coefficient of variation (CV)2.540214256
Kurtosis5.61371634
Mean0.8746700557
Median Absolute Deviation (MAD)0
Skewness2.583461782
Sum116311
Variance4.936614511
MonotocityNot monotonic
2021-03-18T15:56:37.578080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0110523
83.1%
57878
 
5.9%
94946
 
3.7%
22676
 
2.0%
32079
 
1.6%
11803
 
1.4%
61157
 
0.9%
81149
 
0.9%
4563
 
0.4%
7104
 
0.1%
ValueCountFrequency (%)
-199
 
0.1%
0110523
83.1%
11803
 
1.4%
22676
 
2.0%
32079
 
1.6%
ValueCountFrequency (%)
94946
3.7%
81149
 
0.9%
7104
 
0.1%
61157
 
0.9%
57878
5.9%

situ
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.328350015
Minimum-1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-18T15:56:37.648914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum8
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.135904859
Coefficient of variation (CV)0.8551246631
Kurtosis19.02459798
Mean1.328350015
Median Absolute Deviation (MAD)0
Skewness4.18596441
Sum176640
Variance1.29027985
MonotocityNot monotonic
2021-03-18T15:56:37.715997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1117931
88.7%
37529
 
5.7%
82067
 
1.6%
22033
 
1.5%
41180
 
0.9%
51033
 
0.8%
6977
 
0.7%
-1227
 
0.2%
ValueCountFrequency (%)
-1227
 
0.2%
1117931
88.7%
22033
 
1.5%
37529
 
5.7%
41180
 
0.9%
ValueCountFrequency (%)
82067
 
1.6%
6977
 
0.7%
51033
 
0.8%
41180
 
0.9%
37529
5.7%

vma
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.54586131
Minimum-1
Maximum800
Zeros2
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-18T15:56:37.799087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile30
Q150
median50
Q380
95-th percentile110
Maximum800
Range801
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.02083426
Coefficient of variation (CV)0.4065396719
Kurtosis50.02038147
Mean61.54586131
Median Absolute Deviation (MAD)0
Skewness3.040271305
Sum8184184
Variance626.0421472
MonotocityNot monotonic
2021-03-18T15:56:37.894598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5072685
54.7%
8020365
 
15.3%
709958
 
7.5%
908807
 
6.6%
308313
 
6.3%
1105695
 
4.3%
1303707
 
2.8%
-11899
 
1.4%
60482
 
0.4%
20313
 
0.2%
Other values (24)753
 
0.6%
ValueCountFrequency (%)
-11899
1.4%
02
 
< 0.1%
125
 
< 0.1%
244
 
< 0.1%
314
 
< 0.1%
ValueCountFrequency (%)
8001
 
< 0.1%
7004
 
< 0.1%
6001
 
< 0.1%
5603
 
< 0.1%
50037
< 0.1%

Interactions

2021-03-18T15:55:04.908183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.018480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.122076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.219387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.316680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.414366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.512661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.610483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.709713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.815432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:05.912865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.010591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.109323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.200503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.303391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.406158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.506160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.612317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.711064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.815136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:06.920597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.009784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.096666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.318893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.425256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.521157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.620740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.727279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.823329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:07.916579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.005717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.093862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.180808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.268363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.356225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.446054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.541137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.630700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.722764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.815924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.897321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:08.990191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.082138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.171527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.267816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.354846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.448955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.544685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.624850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.706166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.807460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.895437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:09.983464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.071013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.167612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.268615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.364809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.460151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.684197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.793820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.889391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:10.984689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.097851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.230675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.361406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.498158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.605406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.693955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.805866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:11.910072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:12.011124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:12.131454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:12.227541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:12.332201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:55:12.435769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-18T15:56:13.507591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:13.606477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:13.705113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:13.796636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:13.893288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:13.985026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.077446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.169108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.262612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.357986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.452864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.554305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.648201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.742074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.839608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:14.926240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.024806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.123044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.218481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.320160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.412965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.516007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.618760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.704537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.789745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.895104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:15.989510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.084554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.187304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.289080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.384673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.484294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.579648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.675003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.768640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.861727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:16.955714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.051934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.154026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.249391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.344384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.441454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.530135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.639057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.738078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.834292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:17.938861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.034886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.139321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.242666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.331394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.426916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.544003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.637750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.731114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.837360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:18.951975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.061044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.174474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.280797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.389489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.499950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.609247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.720707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.831987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:19.945877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.053545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.160983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.270047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.374885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.481680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.591419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.699447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.814288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:20.918754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.031675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.145205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.242283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.325908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.436519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.543419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T15:56:21.651937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-18T15:56:38.040294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-18T15:56:38.342440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-18T15:56:38.642564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-18T15:56:38.948964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-18T15:56:39.213182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-18T15:56:22.197595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-18T15:56:24.737248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-18T15:56:26.090974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-18T15:56:26.462492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Num_Accid_vehiculenum_vehplacecatugravsexean_naistrajetsecu1secu2secu3locpactpetatpsenccatvobsobsmchocmanvmotoroccutcjourmoisanhrmnlumdepcomaggintatmcoladrlatlongcatrvoiev1v2circnbvvospprofprpr1planlartpclarroutsurfinfrasituvma
0201900000001138 306 524B0122422002010-1-1-1-127025231NaN3011201901:30493930531112AUTOROUTE A348,89621002,4701200130.0NaN3100169002NaNNaN12170
1201900000001138 306 524B0111421993510-1-1-1-127025231NaN3011201901:30493930531112AUTOROUTE A348,89621002,4701200130.0NaN3100169002NaNNaN12170
2201900000001138 306 525A0111111959010-1-1-1-1217103111NaN3011201901:30493930531112AUTOROUTE A348,89621002,4701200130.0NaN3100169002NaNNaN12170
3201900000002138 306 523A0111421994010-1-1-1-11740101NaN3011201902:50393930661116AUTOROUTE A148,93070002,3688000110.0NaN120438452NaNNaN10170
4201900000003138 306 520A0111111996010-1-10-11702121NaN2811201915:15192920361114AUTOROUTE A8648,93587182,31917441860.0NaN3801105003NaNNaN10190
5201900000003138 306 520A0122421930910-1-10-11702121NaN2811201915:15192920361114AUTOROUTE A8648,93587182,31917441860.0NaN3801105003NaNNaN10190
6201900000003138 306 521B0111411995910-1-10-11710421NaN2811201915:15192920361114AUTOROUTE A8648,93587182,31917441860.0NaN3801105003NaNNaN10190
7201900000003138 306 522C0111111966110-1-10-117024236NaN2811201915:15192920361114AUTOROUTE A8648,93587182,31917441860.0NaN3801105003NaNNaN10190
8201900000004138 306 517A0111111993018-1-10-127024231NaN3011201920:20594940691114A448,81732952,4281502140.0NaN350122991NaNNaN10190
9201900000004138 306 518B0111111968518-1-10-127024231NaN3011201920:20594940691114A448,81732952,4281502140.0NaN350122991NaNNaN10190

Last rows

Num_Accid_vehiculenum_vehplacecatugravsexean_naistrajetsecu1secu2secu3locpactpetatpsenccatvobsobsmchocmanvmotoroccutcjourmoisanhrmnlumdepcomaggintatmcoladrlatlongcatrvoiev1v2circnbvvospprofprpr1planlartpclarroutsurfinfrasituvma
132967201900058836137 982 136B0111411992410-1-10-12702601NaN3011201909:00169692881152A43 13.029 A 15.97145,66666005,05612001430.0NaN1301155001NaNNaN231130
132968201900058836137 982 137A0111411973510-1-10-127321211NaN3011201909:00169692881152A43 13.029 A 15.97145,66666005,05612001430.0NaN1301155001NaNNaN231130
132969201900058836137 982 137A0122411968510-1-10-127321211NaN3011201909:00169692881152A43 13.029 A 15.97145,66666005,05612001430.0NaN1301155001NaNNaN231130
132970201900058837137 982 133A0111121972114-1-1-1-11702423NaN2711201907:50167674821184Autoroute A.3548,57690007,72690001350.0NaN12013032821NaNNaN20190
132971201900058837137 982 134B0111411964114-1-1-1-11702121NaN2711201907:50167674821184Autoroute A.3548,57690007,72690001350.0NaN12013032821NaNNaN20190
132972201900058837137 982 135C0111421988110-1-1-1-11702121NaN2711201907:50167674821184Autoroute A.3548,57690007,72690001350.0NaN12013032821NaNNaN20190
132973201900058838137 982 132A0111411998910-1-1-1-127301210NaN3011201902:41494940211116AUTOROUTE A6A48,77170002,3457600160.0A330151991NaNNaN10190
132974201900058839137 982 131A0111311979020-1-1-1-123300711NaN3011201915:20178786401117A86 EXT48,77728902,22375901860.0NaN110159993NaNNaN10150
132975201900058840137 982 129B0111411974010-1-10-111002402NaN2911201920:50392920471112A1348,83512362,17511011130.0NaN130166991NaNNaN102110
132976201900058840137 982 130A011111197101-1-100-111002101NaN2911201920:50392920471112A1348,83512362,17511011130.0NaN130166991NaNNaN102110